## 1. What is neural network?
Neural networks are a type of artificial intelligence (AI) system inspired by the human brain. A neural network consists of many simple processing units called neurons. Neurons communicate with each other by passing signals along their connections. These connections are weighted according to how strongly they transmit a signal. Each neuron receives input from other neurons, processes this information, and then produces output based on its own internal state.
## 2. How does a neural network learn?
A neural network learns by adjusting the weights between its neurons. As the network receives inputs, it compares these inputs to the outputs produced by previous layers. If the difference between the two is greater than some threshold value, the weight connecting the two neurons is increased. On the other hand, if the difference is less than the threshold, the connection is decreased. In this way, the network adjusts the weights until the error between the actual output and desired output is minimized.
## 3. Why do we need neural networks?
The idea behind using neural networks is to mimic the way our brains learn. Our brains have evolved over millions of years to perform complex tasks such as recognizing faces, understanding language, and playing chess. We believe that AI systems should be able to accomplish similar feats. However, unlike humans, computers don't have any innate knowledge about the world. To teach a computer what things look like, sound like, feel like, smell like, taste like, etc., requires us to program it. Programming a machine to recognize objects is relatively straightforward, but teaching it to understand language is much harder.
## 4. What are the different types of neural networks?
There are three major categories of neural networks: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a neural network to predict future events given past data. Unsupervised learning doesn't require any prior knowledge about the data being processed. Reinforcement learning uses feedback to adjust the behavior of a neural network.
## 5. What are the applications of neural networks?
Neural networks have been applied to a wide range of problems including speech recognition, handwriting recognition, image classification, and natural language processing. Deep learning is a subset of neural networks that use deep architectures. Deep learning has had great success in fields such as object detection, speech recognition, and translation.
Top comments (0)